{"title":"Graphics Cards","description":"\u003cp\u003eHigh-VRAM graphics cards for local AI, rendering and gaming: NVIDIA RTX PRO Blackwell and GeForce, AMD Radeon PRO and AI PRO, and Intel Arc Pro.\u003c\/p\u003e","products":[{"product_id":"nvidia-rtx-pro-6000-blackwell-workstation-edition-96gb","title":"NVIDIA RTX PRO 6000 Blackwell Workstation Edition (96GB)","description":"\u003cp\u003eThe NVIDIA RTX PRO 6000 Blackwell Workstation Edition is NVIDIA’s flagship professional GPU. It pairs the full Blackwell GB202 die with 96GB of ECC-protected GDDR7, enough memory to hold entire large language models, massive point clouds or feature-film-resolution scenes without offloading to system RAM. It suits studios, labs and engineering teams that need workstation-class reliability and datacenter-class capacity in a single desktop tower.\u003c\/p\u003e\u003ch2\u003eWho it's for\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003eRunning 70B-parameter LLMs locally in reduced precision on a single card\u003c\/li\u003e\n\u003cli\u003eFine-tuning mid-size models without gradient checkpointing workarounds, or keeping several diffusion and vision models resident for production pipelines\u003c\/li\u003e\n\u003cli\u003eRendering film-resolution scenes in V-Ray, Arnold, Octane, Redshift and OptiX-based renderers with geometry, textures and simulation caches held in GPU memory\u003c\/li\u003e\n\u003cli\u003eCFD, FEA and digital-twin simulation, where dataset size rather than raw FLOPS is usually the limit\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch2\u003eKey features\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eFull Blackwell GB202 die:\u003c\/strong\u003e 24,064 CUDA cores across 188 streaming multiprocessors, the largest professional Blackwell configuration NVIDIA ships, built on TSMC’s 4NP process.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e96GB GDDR7 with ECC:\u003c\/strong\u003e the largest memory pool of any NVIDIA workstation GPU, on a 512-bit bus, with error-correcting protection for mission-critical simulation and inference work.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e1,792GB\/s memory bandwidth:\u003c\/strong\u003e feeds the compute cores for real-time AI inference, fluid and structural simulation, and multi-layer 8K compositing.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e752 fifth-generation Tensor Cores:\u003c\/strong\u003e up to 4,000 AI TOPS (FP4 with structured sparsity) and native FP4 support, a Blackwell capability that accelerates modern LLM inference and fine-tuning and is an architectural step beyond the previous RTX 6000 Ada generation.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e188 fourth-generation RT Cores:\u003c\/strong\u003e hardware-accelerated ray and path tracing for production rendering, architectural visualization and real-time look development.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e125 TFLOPS FP32:\u003c\/strong\u003e strong single-precision compute for CAD, simulation and scientific workloads.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e600W configurable TDP:\u003c\/strong\u003e one 16-pin PCIe CEM5 power connector, with power tunable by system integrators to balance thermals, acoustics and density in workstation or server chassis.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQuad DisplayPort 2.1b outputs:\u003c\/strong\u003e drive up to four high-resolution displays, including 8K panels, from one card.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch2\u003eSpecifications\u003c\/h2\u003e\u003ctable\u003e\n\u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eSpec\u003c\/th\u003e\n\u003cth\u003eDetail\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eArchitecture\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Blackwell (GB202, full die), TSMC 4NP\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCUDA Cores\u003c\/td\u003e\n\u003ctd\u003e24,064\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTensor Cores\u003c\/td\u003e\n\u003ctd\u003e752 (5th-generation, native FP4)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Cores\u003c\/td\u003e\n\u003ctd\u003e188 (4th-generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory\u003c\/td\u003e\n\u003ctd\u003e96GB GDDR7 with ECC, 512-bit bus\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Bandwidth\u003c\/td\u003e\n\u003ctd\u003e1,792GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP32 Compute\u003c\/td\u003e\n\u003ctd\u003e125 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Performance\u003c\/td\u003e\n\u003ctd\u003eUp to 4,000 AI TOPS (FP4 sparse)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower\u003c\/td\u003e\n\u003ctd\u003e600W configurable TDP, 1x 16-pin PCIe CEM5 connector\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInterface\u003c\/td\u003e\n\u003ctd\u003ePCIe 5.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay Outputs\u003c\/td\u003e\n\u003ctd\u003e4x DisplayPort 2.1b\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm Factor\u003c\/td\u003e\n\u003ctd\u003eDual-slot, workstation blower\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLaunch\u003c\/td\u003e\n\u003ctd\u003eMarch 2025, MSRP $8,565\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\u003ch2\u003eGood to know\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003eThis is a professional card, not a gaming GPU. It ships with NVIDIA’s Studio and RTX Enterprise driver branches and is validated against major CAD, DCC and AI frameworks.\u003c\/li\u003e\n\u003cli\u003eOne card with this much memory means no manual model sharding and no NVLink bridge management.\u003c\/li\u003e\n\u003cli\u003eAt 600W and dual-slot, it fits standard workstation chassis without the multi-card cooling and power demands of a datacenter GPU build.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eChoose this card when VRAM is your bottleneck: a language model that will not fit, a scene that keeps swapping to disk, or a simulation mesh you have had to decimate. It removes that ceiling in one upgrade.\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":58232537481598,"sku":"NVIDIA-RTXPRO6000-96GB","price":13250.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1022\/4468\/0062\/files\/gpu-rtx-pro-6000_e1a9f438-abcb-4c56-bc89-ffbefc99805b.jpg?v=1792094031"},{"product_id":"nvidia-geforce-rtx-5090-founders-edition-32gb","title":"NVIDIA GeForce RTX 5090 Founders Edition (32GB)","description":"\u003cp\u003eThe NVIDIA GeForce RTX 5090 Founders Edition is NVIDIA’s flagship Blackwell-generation consumer GPU, the largest GeForce die NVIDIA has ever shipped, in a Founders Edition cooler engineered to fit that much silicon into two slots. It suits gamers and creators who refuse to compromise: native 4K at maximum settings, 8K footage editing, and local diffusion models and LLMs on your own hardware.\u003c\/p\u003e\n\u003ch2\u003eWho it's for\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eNative 4K gaming with ray tracing maxed out, or high-refresh 1440p and 4K monitors\u003c\/li\u003e\n\u003cli\u003e8K video timelines, multi-layer compositing in DaVinci Resolve or Premiere, and large 3D scenes in Blender or Unreal Engine\u003c\/li\u003e\n\u003cli\u003eRunning Stable Diffusion and fine-tuning smaller LLMs locally\u003c\/li\u003e\n\u003cli\u003eInference on quantized 30B-class models that would spill to system RAM on a 24GB card\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eKey features\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e21,760 CUDA cores:\u003c\/strong\u003e the largest GeForce GPU configuration NVIDIA has produced, on a custom TSMC 4NP process, a substantial generational leap over the RTX 4090 (21,760 vs 16,384 cores).\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e32GB GDDR7 on a 512-bit bus:\u003c\/strong\u003e memory capacity and bandwidth for 4K\/8K textures, huge open-world scenes and local AI models that would choke a 24GB card.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e98MB L2 cache:\u003c\/strong\u003e an expanded cache hierarchy that keeps the compute cores fed and reduces trips to memory.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e680 fifth-generation Tensor Cores:\u003c\/strong\u003e up to 3,352 AI TOPS, powering DLSS 4.5 with Multi Frame Generation for much higher frame rates at native-quality image fidelity.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e170 fourth-generation RT Cores:\u003c\/strong\u003e full-scene path tracing in supported titles without the frame-rate penalty older RT hardware imposed.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e104.8 TFLOPS FP32 shader compute:\u003c\/strong\u003e a generational jump in raw rasterization and compute throughput over Ada Lovelace.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e2.41GHz boost clock:\u003c\/strong\u003e high clocks despite the core count, thanks to the refined 4NP process node.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eNVENC\/NVDEC with AV1 support:\u003c\/strong\u003e cuts export and streaming times significantly versus CPU-only workflows.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCompact 2-slot Founders Edition design:\u003c\/strong\u003e vapor-chamber cooling and flow-through design, validated as a matched pair with the card’s power delivery, in a footprint that fits SFF and standard ATX builds, unlike the triple-slot coolers common on partner cards this powerful.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eSpecifications\u003c\/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eSpec\u003c\/th\u003e\n\u003cth\u003eDetail\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eArchitecture\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Blackwell, TSMC 4NP\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCUDA cores\u003c\/td\u003e\n\u003ctd\u003e21,760\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTensor Cores\u003c\/td\u003e\n\u003ctd\u003e680 (5th-generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Cores\u003c\/td\u003e\n\u003ctd\u003e170 (4th-generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory\u003c\/td\u003e\n\u003ctd\u003e32GB GDDR7, 512-bit bus, 98MB L2 cache\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP32 compute\u003c\/td\u003e\n\u003ctd\u003e~104.8 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI performance\u003c\/td\u003e\n\u003ctd\u003eUp to 3,352 AI TOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBoost clock\u003c\/td\u003e\n\u003ctd\u003e2.41GHz\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTotal graphics power\u003c\/td\u003e\n\u003ctd\u003e575W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower connector\u003c\/td\u003e\n\u003ctd\u003e1x 16-pin (12V-2×6)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInterface\u003c\/td\u003e\n\u003ctd\u003ePCIe 5.0\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay outputs\u003c\/td\u003e\n\u003ctd\u003eDisplayPort 2.1b, up to 8K @ 165Hz\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRecommended PSU\u003c\/td\u003e\n\u003ctd\u003e1000W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLaunch\u003c\/td\u003e\n\u003ctd\u003eJanuary 2025, MSRP $1,999\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003ch2\u003eGood to know\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eTotal graphics power is 575W, so a capable 1000W PSU and a case with good airflow are non-negotiable.\u003c\/li\u003e\n\u003cli\u003eFor local AI it will not match a dedicated 96GB workstation card on the largest models, but for generative AI alongside gaming and creative work no other consumer GPU offers this much memory and compute in one card.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChoose the RTX 5090 when you want the most capable GPU NVIDIA has put in a GeForce box and have the power supply and airflow to match. If 24GB of memory is enough for you, the RTX 4090 is the other option.\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":58232537514366,"sku":"NVIDIA-RTX5090-FE","price":1999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1022\/4468\/0062\/files\/gpu-rtx-5090_10f6e9ed-a914-4145-a3a4-fbfe6cf0ce27.jpg?v=1792094032"},{"product_id":"nvidia-geforce-rtx-4090-founders-edition-24gb","title":"NVIDIA GeForce RTX 4090 Founders Edition (24GB)","description":"\u003cp\u003eThe NVIDIA GeForce RTX 4090 Founders Edition is the Ada Lovelace flagship, and two-plus years after launch it still holds its own against everything short of NVIDIA’s newest Blackwell generation. It suits gamers who want maxed-out 4K performance, creators pushing 8K video and large 3D scenes, and enthusiasts running local AI models.\u003c\/p\u003e\n\u003ch2\u003eWho it's for\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eNative 4K gaming with ray tracing, and high-refresh 1440p with plenty of headroom\u003c\/li\u003e\n\u003cli\u003e8K video editing, dense Blender and Unreal Engine scenes and multi-layer compositing\u003c\/li\u003e\n\u003cli\u003eRunning Stable Diffusion and similar image models at full speed in 24GB\u003c\/li\u003e\n\u003cli\u003eInference on quantized LLMs in the 13B–34B parameter range without exotic offloading tricks\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eKey features\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e16,384 CUDA cores:\u003c\/strong\u003e massive parallel compute on a custom TSMC 4N process, still enough to lead every non-Blackwell GPU in raw shader throughput.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e24GB GDDR6X on a 384-bit bus:\u003c\/strong\u003e 21Gbps for over 1TB\/s of memory bandwidth, handling 4K\/8K textures, large open-world scenes and local AI models up to the mid-teens in billions of parameters.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e72MB L2 cache:\u003c\/strong\u003e a large on-die cache that reduces memory-bound stalls in games and compute workloads.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e512 fourth-generation Tensor Cores:\u003c\/strong\u003e up to 1,321 AI TOPS, driving DLSS 3 Frame Generation and local inference for Stable Diffusion, LLMs and other generative AI.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e128 third-generation RT Cores:\u003c\/strong\u003e up to 191 TFLOPS of ray tracing performance for fully path-traced titles like Cyberpunk 2077 and Alan Wake 2.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e82.6 TFLOPS FP32 shader compute:\u003c\/strong\u003e a substantial generational gain over the previous RTX 30-series flagship, still highly competitive today.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e2.52GHz boost clock:\u003c\/strong\u003e high out-of-the-box clocks with overclocking headroom on partner and Founders Edition boards alike.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAV1 hardware encode and decode:\u003c\/strong\u003e high-quality hardware video encoding that cuts export times and improves streaming quality versus older codecs.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFounders Edition cooler:\u003c\/strong\u003e NVIDIA’s own dual-axial-flow cooler and vapor chamber design keeps the 450W TDP in check without the bulk of some triple-slot partner cards.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eSpecifications\u003c\/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eSpec\u003c\/th\u003e\n\u003cth\u003eDetail\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eArchitecture\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Ada Lovelace, TSMC 4N\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCUDA cores\u003c\/td\u003e\n\u003ctd\u003e16,384\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTensor Cores\u003c\/td\u003e\n\u003ctd\u003e512 (4th-generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Cores\u003c\/td\u003e\n\u003ctd\u003e128 (3rd-generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory\u003c\/td\u003e\n\u003ctd\u003e24GB GDDR6X, 384-bit bus, 21Gbps, 72MB L2 cache\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory bandwidth\u003c\/td\u003e\n\u003ctd\u003e~1,008GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP32 compute\u003c\/td\u003e\n\u003ctd\u003e82.6 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI performance\u003c\/td\u003e\n\u003ctd\u003eUp to 1,321 AI TOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBoost clock\u003c\/td\u003e\n\u003ctd\u003e2.52GHz\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTotal graphics power\u003c\/td\u003e\n\u003ctd\u003e450W (transient spikes up to 600W)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInterface\u003c\/td\u003e\n\u003ctd\u003ePCIe 4.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay outputs\u003c\/td\u003e\n\u003ctd\u003eDisplayPort 1.4a, HDMI 2.1a\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRecommended PSU\u003c\/td\u003e\n\u003ctd\u003e850W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLaunch\u003c\/td\u003e\n\u003ctd\u003eOctober 2022, original MSRP $1,599\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003ch2\u003eGood to know\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003ePair it with an 850W-or-better power supply and a case with decent airflow. The card is known for transient power spikes of up to 600W.\u003c\/li\u003e\n\u003cli\u003eAt native 4K with ray tracing enabled it delivers high, stable frame rates in demanding current titles, and DLSS 3 Frame Generation extends that lead in supported games.\u003c\/li\u003e\n\u003cli\u003eThe NVENC AV1 encoder significantly speeds up exports and live streaming versus CPU-based encoding or older hardware encoders.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChoose the RTX 4090 for uncompromising 4K gaming, creative work and local AI without stepping up to Blackwell pricing. Only NVIDIA’s newest Blackwell flagships outrun it.\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":58232537547134,"sku":"NVIDIA-RTX4090-FE","price":1599.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1022\/4468\/0062\/files\/gpu-rtx-4090_ca2528f4-691c-4fd1-962b-45a9b462ecf9.jpg?v=1792094034"},{"product_id":"amd-radeon-rx-7900-xtx-24gb","title":"AMD Radeon RX 7900 XTX (24GB)","description":"\u003cp\u003eThe AMD Radeon RX 7900 XTX is AMD’s flagship RDNA 3 gaming card and the first consumer GPU to use a chiplet design, giving it a 24GB memory pool and near-flagship performance at a price well below the equivalent NVIDIA tier. It suits gamers chasing high-refresh 4K, streamers who need encode headroom and Linux users who want a fully open driver stack.\u003c\/p\u003e\n\u003ch2\u003eWho it's for\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eHigh-refresh native 4K gaming where rasterization performance and VRAM matter most\u003c\/li\u003e\n\u003cli\u003eStreamers and content creators using AMD’s dual encode engines and OpenCL\/HIP compute in supported applications\u003c\/li\u003e\n\u003cli\u003eLocal image generation and smaller LLM inference, which a 12GB or 16GB card cannot do as comfortably\u003c\/li\u003e\n\u003cli\u003eLinux users who want ROCm and open-source PyTorch-based AI\/ML development\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eKey features\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eChiplet-based Navi 31 GPU:\u003c\/strong\u003e a 5nm graphics compute die paired with six 6nm memory cache dies (MCDs), the first gaming GPU to split compute and memory logic across separate chiplets for better yields and cost efficiency.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e96 Compute Units, 6,144 Stream Processors:\u003c\/strong\u003e high-throughput parallel compute for rendering, ray tracing and general GPU workloads.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e24GB GDDR6 on a 384-bit bus:\u003c\/strong\u003e more memory than NVIDIA’s equivalent-tier competition, at up to 20Gbps for 960GB\/s of raw bandwidth.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e96MB AMD Infinity Cache:\u003c\/strong\u003e a large on-die cache that lifts effective memory bandwidth up to 3,500GB\/s, keeping the GPU fed at 4K and beyond without a wider memory bus.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e96 second-generation Ray Accelerators and 192 AI Accelerators:\u003c\/strong\u003e dedicated hardware for ray tracing and AI-assisted upscaling (FSR) workloads.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eUp to 2,500MHz boost clock:\u003c\/strong\u003e 2,300MHz game clock, for consistent frame rates under load.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e61.4 TFLOPS FP32:\u003c\/strong\u003e strong raw compute for gaming and GPU-accelerated creative applications.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eROCm support on Linux:\u003c\/strong\u003e an open-source path into PyTorch-based AI\/ML development, without proprietary CUDA tooling.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eSpecifications\u003c\/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eSpec\u003c\/th\u003e\n\u003cth\u003eDetail\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eArchitecture\u003c\/td\u003e\n\u003ctd\u003eAMD RDNA 3 (Navi 31), chiplet design: 5nm GCD + 6nm MCDs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute units\u003c\/td\u003e\n\u003ctd\u003e96 (6,144 Stream Processors)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAccelerators\u003c\/td\u003e\n\u003ctd\u003e96 Ray Accelerators (2nd-generation) \/ 192 AI Accelerators\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory\u003c\/td\u003e\n\u003ctd\u003e24GB GDDR6, 384-bit bus, 20Gbps\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory bandwidth\u003c\/td\u003e\n\u003ctd\u003e960GB\/s (up to 3,500GB\/s effective with Infinity Cache)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInfinity Cache\u003c\/td\u003e\n\u003ctd\u003e96MB\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP32 compute\u003c\/td\u003e\n\u003ctd\u003e61.4 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eClocks\u003c\/td\u003e\n\u003ctd\u003e2,300MHz game \/ up to 2,500MHz boost\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTotal board power\u003c\/td\u003e\n\u003ctd\u003e355W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower connectors\u003c\/td\u003e\n\u003ctd\u003e2x 8-pin PCIe (reference design)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInterface\u003c\/td\u003e\n\u003ctd\u003ePCIe 4.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay outputs\u003c\/td\u003e\n\u003ctd\u003eDisplayPort 2.1, HDMI 2.1, USB-C (reference design)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLaunch\u003c\/td\u003e\n\u003ctd\u003eDecember 2022, original MSRP $999\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003ch2\u003eGood to know\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eAt native 4K it trades blows with NVIDIA’s previous-generation flagship class while typically costing less. The trade is that DLSS and ray tracing quality still generally lead FSR and AMD’s ray accelerators in the most demanding path-traced titles.\u003c\/li\u003e\n\u003cli\u003eROCm is open and improving, but it does not yet have the plug-and-play maturity of CUDA for AI\/ML work, particularly on Windows. Support is strongest on Linux, where ROCm is officially validated for this GPU.\u003c\/li\u003e\n\u003cli\u003e24GB of VRAM holds up better than 16GB competitors as texture and asset sizes grow, and titles that felt VRAM-constrained on an 8GB or 12GB card will not be an issue. Games running at 1440p today have room to move up to native 4K.\u003c\/li\u003e\n\u003cli\u003eThe Adrenalin driver suite offers strong feature parity for streaming, recording and performance tuning.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChoose the RX 7900 XTX for the most VRAM and raw performance per pound in AMD’s lineup, if you are comfortable with FSR instead of DLSS. If best-in-class ray tracing or CUDA matters more, look at NVIDIA.\u003c\/p\u003e","brand":"AMD","offers":[{"title":"Default Title","offer_id":58232537612670,"sku":"AMD-RX7900XTX","price":899.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1022\/4468\/0062\/files\/gpu-rx-7900xtx.jpg?v=1792094035"},{"product_id":"amd-radeon-pro-w7900-48gb","title":"AMD Radeon PRO W7900 (48GB)","description":"\u003cp\u003eThe AMD Radeon PRO W7900 is AMD’s flagship workstation graphics card. Its defining feature is 48GB of ECC-protected memory on the full RDNA 3 Navi 31 die, in a dual-slot form factor that fits real workstation chassis. It suits studios and engineering teams running large AI models, professional rendering pipelines or scientific visualization workloads that outgrow a 24GB consumer card.\u003c\/p\u003e\n\u003ch2\u003eWho it's for\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eRunning 30B-parameter class LLMs and keeping several models resident for a production inference pipeline\u003c\/li\u003e\n\u003cli\u003eFine-tuning mid-size open-weight models without chunking datasets or offloading to system memory\u003c\/li\u003e\n\u003cli\u003eArchitectural and product visualization, full-resolution VFX shots and simulation caches held entirely in GPU memory\u003c\/li\u003e\n\u003cli\u003eEngineering firms standardizing on one ECC-protected, ISV-certified card per seat\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eKey features\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e48GB ECC GDDR6:\u003c\/strong\u003e the full Navi 31 memory configuration on a 384-bit bus, with error-correcting protection for professional reliability, for LLMs, production scenes and massive point-cloud, 3D scan or CAD datasets on-card.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e96 Compute Units, 6,144 Stream Processors:\u003c\/strong\u003e the full Navi 31 die, set up for workstation workloads rather than gaming clocks.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e96 second-generation Ray Accelerators, 192 AI Accelerators:\u003c\/strong\u003e dedicated hardware for professional ray-traced rendering and AI-assisted workflows.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e864GB\/s memory bandwidth:\u003c\/strong\u003e keeps the compute units fed when working with the large datasets this memory capacity allows.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e61 TFLOPS FP32 \/ 122 TFLOPS FP16:\u003c\/strong\u003e strong compute for rendering, simulation and mixed-precision AI workloads.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDual-slot workstation design:\u003c\/strong\u003e a more space-efficient footprint than earlier triple-slot professional cards, so integrators can fit up to four W7900s in one multi-GPU workstation.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eROCm certified:\u003c\/strong\u003e validated for professional AI and ML development, with ISV-certified drivers for major CAD, DCC and simulation applications.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQuad DisplayPort 2.1 outputs:\u003c\/strong\u003e three full-size DisplayPort 2.1 plus one mini-DisplayPort 2.1, each capable of 54Gbps, for high-resolution multi-monitor CAD and visualization.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eSpecifications\u003c\/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eSpec\u003c\/th\u003e\n\u003cth\u003eDetail\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eArchitecture\u003c\/td\u003e\n\u003ctd\u003eAMD RDNA 3 (full Navi 31 die)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute units\u003c\/td\u003e\n\u003ctd\u003e96 (6,144 Stream Processors, 192 AI Accelerators, 96 Ray Accelerators)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory\u003c\/td\u003e\n\u003ctd\u003e48GB ECC GDDR6, 384-bit bus\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory bandwidth\u003c\/td\u003e\n\u003ctd\u003e864GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute performance\u003c\/td\u003e\n\u003ctd\u003e61 TFLOPS FP32 \/ 122 TFLOPS FP16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eClocks\u003c\/td\u003e\n\u003ctd\u003e1,855MHz core \/ 2,495MHz boost\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTotal board power\u003c\/td\u003e\n\u003ctd\u003e295W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower connectors\u003c\/td\u003e\n\u003ctd\u003e2x 8-pin PCIe\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInterface\u003c\/td\u003e\n\u003ctd\u003ePCIe 4.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm factor\u003c\/td\u003e\n\u003ctd\u003eDual-slot workstation card, 280mm length\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay outputs\u003c\/td\u003e\n\u003ctd\u003e3x DisplayPort 2.1 + 1x mini-DisplayPort 2.1\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSoftware\u003c\/td\u003e\n\u003ctd\u003eROCm certified, ISV-certified professional drivers\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003ch2\u003eGood to know\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eROCm is officially supported and certified for this card, but the CUDA ecosystem still has a head start in framework coverage, especially for bleeding-edge AI research code that assumes NVIDIA hardware.\u003c\/li\u003e\n\u003cli\u003eTeams already standardized on PyTorch and mainstream inference frameworks have a well-supported path on ROCm.\u003c\/li\u003e\n\u003cli\u003eOne W7900 replaces what would otherwise take two or more 24GB cards, without the extra system complexity, driver overhead or PCIe lane contention of a multi-GPU build.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChoose the W7900 when memory headroom is the problem and your workflows are rendering-, CAD- or mainstream-AI-first. It is a low-risk, high-capacity upgrade; if your code needs CUDA, look elsewhere.\u003c\/p\u003e","brand":"AMD","offers":[{"title":"Default Title","offer_id":58232537940350,"sku":"AMD-W7900","price":3999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1022\/4468\/0062\/files\/gpu-w7900.jpg?v=1792094037"},{"product_id":"amd-radeon-ai-pro-r9700-32gb","title":"AMD Radeon AI PRO R9700 (32GB)","description":"\u003cp\u003eThe AMD Radeon AI PRO R9700 is AMD’s first RDNA 4 workstation card. It gives you 32GB of memory in a 2-slot blower design, aimed at teams who want serious local AI capability without stepping up to a 48GB flagship card or a datacenter GPU. It suits AI developers, data scientists and technical creators who need local inference and fine-tuning headroom on a workstation budget.\u003c\/p\u003e\n\u003ch2\u003eWho it's for\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eRunning quantized LLMs in the 30B+ parameter range locally\u003c\/li\u003e\n\u003cli\u003eFine-tuning smaller models entirely on the card\u003c\/li\u003e\n\u003cli\u003eBuilding and testing RAG pipelines before deploying to cloud infrastructure\u003c\/li\u003e\n\u003cli\u003eMulti-GPU AI workstations that pool memory across several cards in one chassis\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eKey features\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eRDNA 4 architecture (Navi 48):\u003c\/strong\u003e AMD’s newest GPU architecture on TSMC’s 4nm process, a generational leap in AI-specific hardware over the previous RDNA 3 workstation cards.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e32GB GDDR6 on a 256-bit bus:\u003c\/strong\u003e 20Gbps for 640GB\/s of bandwidth, enough for quantized LLMs in the 30B+ parameter range or fine-tuning smaller models on-card.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e64 Compute Units, 4,096 Stream Processors:\u003c\/strong\u003e plus 64 second-generation Ray Accelerators for professional rendering and visualization work.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e128 second-generation AI Accelerators:\u003c\/strong\u003e up to 95.7 TFLOPS of dense FP16 compute and up to 1,531 TOPS of INT4 performance with sparsity, tuned for the low-precision matrix math (INT4, INT8, FP8) of quantized inference.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMulti-GPU scaling:\u003c\/strong\u003e pool memory and compute across multiple R9700 cards for larger models than one card can hold, at a lower cost per gigabyte than a flagship card, provided your framework and workload support multi-GPU sharding.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e2-slot blower cooling:\u003c\/strong\u003e a compact, workstation-friendly form factor that supports dense multi-GPU builds.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQuad DisplayPort 2.1a outputs:\u003c\/strong\u003e full multi-monitor support for engineering and creative work alongside AI workloads.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eROCm on Linux and Windows:\u003c\/strong\u003e an open software stack for AI development, officially supported on both major desktop operating systems.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eSpecifications\u003c\/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eSpec\u003c\/th\u003e\n\u003cth\u003eDetail\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eArchitecture\u003c\/td\u003e\n\u003ctd\u003eAMD RDNA 4 (Navi 48), TSMC 4nm\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute units\u003c\/td\u003e\n\u003ctd\u003e64 (4,096 Stream Processors)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAccelerators\u003c\/td\u003e\n\u003ctd\u003e64 Ray Accelerators (2nd-gen) \/ 128 AI Accelerators (2nd-gen)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory\u003c\/td\u003e\n\u003ctd\u003e32GB GDDR6, 256-bit bus, 20Gbps\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory bandwidth\u003c\/td\u003e\n\u003ctd\u003e640GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute performance\u003c\/td\u003e\n\u003ctd\u003e47.8 TFLOPS FP32 \/ 95.7 TFLOPS FP16 \/ up to 1,531 TOPS INT4 (sparse)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eClocks\u003c\/td\u003e\n\u003ctd\u003e1,620MHz base \/ 2,920MHz boost\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTotal board power\u003c\/td\u003e\n\u003ctd\u003e300W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower connector\u003c\/td\u003e\n\u003ctd\u003e1x 12V-2×6\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInterface\u003c\/td\u003e\n\u003ctd\u003ePCIe 5.0\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm factor\u003c\/td\u003e\n\u003ctd\u003e2-slot blower\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay outputs\u003c\/td\u003e\n\u003ctd\u003e4x DisplayPort 2.1a\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLaunch\u003c\/td\u003e\n\u003ctd\u003eJuly 2025, MSRP $1,299\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003ch2\u003eGood to know\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eROCm is officially supported on this card on Linux and Windows, but framework compatibility and out-of-the-box performance still generally trail CUDA on the newest, most experimental AI research code.\u003c\/li\u003e\n\u003cli\u003eMainstream inference stacks such as llama.cpp, vLLM and PyTorch with ROCm backends are the better fit than bleeding-edge research repos.\u003c\/li\u003e\n\u003cli\u003eThe card has more memory and AI-specific hardware than any consumer gaming GPU at a similar price, and it also suits GPU-accelerated rendering and data-heavy visualization.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChoose the R9700 as a first card for local inference infrastructure, with room to add cards once your workload and frameworks are proven. If you need 48GB on a single card, look at the tier above.\u003c\/p\u003e","brand":"AMD","offers":[{"title":"Default Title","offer_id":58232537973118,"sku":"AMD-R9700","price":1299.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1022\/4468\/0062\/files\/gpu-r9700_629f0734-5b66-4828-a058-c8c40bfa8615.png?v=1792094038"},{"product_id":"intel-arc-pro-b50-16gb","title":"Intel Arc Pro B50 (16GB)","description":"\u003cp\u003eThe Intel Arc Pro B50 is a compact, low-profile workstation GPU built on Intel’s Xe2 “Battlemage” architecture. It packs 16GB of memory and dedicated AI acceleration into a 70W design that needs no external power connector. That makes it a low-risk upgrade for small-form-factor workstations, entry-level AI inference boxes and multi-display CAD or design setups where space and power budget are tight.\u003c\/p\u003e\n\u003ch2\u003eWho it's for\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSmall-form-factor workstations that cannot fit or power a larger professional card\u003c\/li\u003e\n\u003cli\u003eLightweight local inference with smaller open-weight LLMs, image classification models and vision pipelines at the edge\u003c\/li\u003e\n\u003cli\u003eCAD, 2D\/3D design, video editing and light content creation across multiple displays\u003c\/li\u003e\n\u003cli\u003eIT teams refreshing rows of compact workstations without auditing power supplies one system at a time\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eKey features\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eIntel Xe2 “Battlemage” architecture:\u003c\/strong\u003e built on TSMC’s N5 process, Intel’s second-generation discrete GPU architecture with dedicated ray tracing and matrix (XMX) engines for AI workloads.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e16GB GDDR6:\u003c\/strong\u003e more memory than many competing entry workstation cards at this price and power point, useful headroom for CAD models, multi-layer design work and small-to-mid AI models.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e16 Xe2 cores, 128 XMX engines:\u003c\/strong\u003e dedicated matrix hardware alongside 16 ray tracing units, delivering up to 170 TOPS of INT8 performance for AI inference.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e10.65 TFLOPS FP32 compute:\u003c\/strong\u003e solid general-purpose shader performance for CAD, design and light rendering work.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eJust 70W total board power:\u003c\/strong\u003e no external power connector is needed on most builds, as the card draws power entirely from the PCIe slot, so virtually any modern PSU can run one.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLow-profile, dual-slot, single-fan design:\u003c\/strong\u003e fits compact and small-form-factor workstations where larger workstation cards will not physically fit.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePCIe 5.0 x8 interface:\u003c\/strong\u003e a modern interface standard at a fraction of the power draw of higher-end workstation cards.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e224GB\/s memory bandwidth:\u003c\/strong\u003e solid throughput for a card in this power and price class.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eSpecifications\u003c\/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eSpec\u003c\/th\u003e\n\u003cth\u003eDetail\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eArchitecture\u003c\/td\u003e\n\u003ctd\u003eIntel Xe2 “Battlemage,” TSMC N5\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eXe2 cores\u003c\/td\u003e\n\u003ctd\u003e16 (128 XMX engines, 16 ray tracing units)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory\u003c\/td\u003e\n\u003ctd\u003e16GB GDDR6, 128-bit bus\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory bandwidth\u003c\/td\u003e\n\u003ctd\u003e224GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute performance\u003c\/td\u003e\n\u003ctd\u003e10.65 TFLOPS FP32 \/ up to 170 TOPS INT8\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eClocks\u003c\/td\u003e\n\u003ctd\u003e1,700MHz base \/ 2,600MHz boost\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTotal board power\u003c\/td\u003e\n\u003ctd\u003e70W (no external power connector)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInterface\u003c\/td\u003e\n\u003ctd\u003ePCIe 5.0 x8\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm factor\u003c\/td\u003e\n\u003ctd\u003eLow-profile, dual-slot, single-fan\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003ch2\u003eGood to know\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eThis is an entry-level professional card, not a high-end rendering or AI training GPU, and it is not built for heavy fine-tuning.\u003c\/li\u003e\n\u003cli\u003eIntel’s software ecosystem (drivers, ISV certifications, oneAPI tooling) is younger than NVIDIA’s or AMD’s, though it has matured quickly and Intel certifies against major professional applications.\u003c\/li\u003e\n\u003cli\u003eBecause it draws all its power from the PCIe slot, OEM systems can specify it without redesigning power delivery, and fleet upgrades avoid cards that demand a new PSU or extra power connectors (200W-plus cards often do).\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChoose the B50 when power, space and budget are the limits and your workload is CAD, design or light local inference. If you need more memory for larger models, step up to the 24GB B60.\u003c\/p\u003e","brand":"Intel","offers":[{"title":"Default Title","offer_id":58232538005886,"sku":"INTEL-ARCB50","price":299.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1022\/4468\/0062\/files\/gpu-arc-b50_c1d6063f-ef99-422c-b74d-1352979cd632.png?v=1792094039"},{"product_id":"intel-arc-pro-b60-24gb","title":"Intel Arc Pro B60 (24GB)","description":"\u003cp\u003eThe Intel Arc Pro B60 steps up from the B50 to 24GB of memory on the same Xe2 “Battlemage” architecture. It is aimed at AI inference, generative design and 3D workloads where memory capacity matters as much as raw compute. Intel does not manufacture a reference-design B60: every card ships from a board partner (ASRock, Sparkle and others) built to Intel’s reference specification, so this listing is a partner card faithful to that spec rather than an Intel-branded reference board.\u003c\/p\u003e\n\u003ch2\u003eWho it's for\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eRunning quantized LLMs in the 13B–34B range locally\u003c\/li\u003e\n\u003cli\u003eLarger diffusion and vision models that would not fit in a 12GB or 16GB card\u003c\/li\u003e\n\u003cli\u003eProfessional visualization, CAD and generative design with dense 3D scenes and high-resolution textures\u003c\/li\u003e\n\u003cli\u003eTeams that want an affordable path to 48GB of pooled memory by pairing two cards\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eKey features\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e24GB GDDR6 on a 192-bit bus:\u003c\/strong\u003e up to 456GB\/s of bandwidth, well ahead of most cards in its price class, enough to hold large quantized AI models or complex 3D scenes entirely in VRAM.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e20 Xe2 cores, 160 XMX engines:\u003c\/strong\u003e a step up from the B50’s core count, with 20 dedicated ray tracing units for professional visualization work.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e197 TOPS INT8 AI performance:\u003c\/strong\u003e dedicated matrix acceleration for the low-precision math that dominates modern LLM and vision-model inference.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e12.28 TFLOPS FP32 compute:\u003c\/strong\u003e solid general shader performance for CAD, rendering and design applications.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMulti-GPU ready:\u003c\/strong\u003e pairs of B60 cards can combine (Intel’s “Battlemage” dual-GPU and Project Battlematrix configurations) to pool up to 48GB of addressable memory for larger models.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePCIe 5.0 x8 interface:\u003c\/strong\u003e modern interface bandwidth with efficient, scalable power delivery.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e120-200W total board power:\u003c\/strong\u003e a wide, partner-dependent envelope that lets board vendors tune for efficiency or maximum clocks.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eSpecifications\u003c\/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eSpec\u003c\/th\u003e\n\u003cth\u003eDetail\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eArchitecture\u003c\/td\u003e\n\u003ctd\u003eIntel Xe2 “Battlemage,” TSMC N5\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eXe2 cores\u003c\/td\u003e\n\u003ctd\u003e20 (160 XMX engines, 20 ray tracing units)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory\u003c\/td\u003e\n\u003ctd\u003e24GB GDDR6, 192-bit bus\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory bandwidth\u003c\/td\u003e\n\u003ctd\u003eUp to 456GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute performance\u003c\/td\u003e\n\u003ctd\u003e12.28 TFLOPS FP32 \/ up to 197 TOPS INT8\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTotal board power\u003c\/td\u003e\n\u003ctd\u003e120-200W (partner-dependent)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInterface\u003c\/td\u003e\n\u003ctd\u003ePCIe 5.0 x8\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMulti-GPU\u003c\/td\u003e\n\u003ctd\u003ePairable for up to 48GB pooled memory\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAvailability\u003c\/td\u003e\n\u003ctd\u003eBoard-partner card built to Intel reference specification (no Intel-branded reference card exists)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003ch2\u003eGood to know\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eBecause every B60 ships from a board partner, exact clocks, cooling and connector layout vary slightly by vendor. All cards adhere to Intel’s core specification above.\u003c\/li\u003e\n\u003cli\u003eIntel’s software ecosystem is newer than CUDA’s. oneAPI, OpenVINO and the driver stack support mainstream AI and CAD frameworks, but bleeding-edge research code that assumes NVIDIA hardware will need more setup work.\u003c\/li\u003e\n\u003cli\u003eStandard, well-supported tools such as llama.cpp, PyTorch with Intel extensions, OpenVINO-based inference and mainstream CAD applications are the comfortable fit.\u003c\/li\u003e\n\u003cli\u003eIntel has been expanding ISV certifications and driver validation for the Arc Pro line.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChoose the B60 over the B50 when 24GB of memory is the deciding factor for your models or scenes. Choose a CUDA card instead if your code assumes NVIDIA hardware.\u003c\/p\u003e","brand":"Intel","offers":[{"title":"Default Title","offer_id":58232538038654,"sku":"INTEL-ARCB60","price":599.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1022\/4468\/0062\/files\/gpu-arc-b60_90788c39-e1f4-4f95-92ab-78d16424b6c8.png?v=1792094041"}],"url":"https:\/\/computahardware.net\/collections\/graphics-cards.oembed","provider":"ComputaHardware","version":"1.0","type":"link"}